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Multiagent and Grid Systems

Enhancing distributed agent environments with quantum multi-agent systems and protocols

A Jenefa, K Vidhya, Antony Taurshia, Vakada Naveen, Bessy M. Kuriakose, V. Vijula

Published Aug 12, 2024• 6 citations
Publisher

Abstract

The utilization of Quantum Multi-Agent Systems (MAS) and Quantum Protocols in distributed agent environments has gained attention due to the need for enhanced protocol efficiency in quantum computing applications. Conventional methods often face limitations in achieving optimal performance, hindering the full potential of quantum computing in distributed settings. Existing approaches lack the necessary robustness to fully exploit the advantages offered by Quantum MAS, leading to inefficiencies in computational performance within distributed agent environments. In this context, we propose a novel Quantum MAS framework, which harnesses the principles of quantum superposition, entanglement, and advanced Quantum Protocols, including the quantum key distribution mechanism. The framework facilitates collaborative decision-making among agents through the utilization of joint quantum states and enables seamless synchronization of actions via the entanglement operator. The computational efficiency is optimized using quantum gate operations, thereby enhancing the overall computational performance in the distributed agent environment. We quantify the efficiency, showcasing the significant improvements achieved by the proposed Quantum MAS framework. Our research employs diverse datasets, including synthetic and real-world data, to comprehensively evaluate the performance and efficacy of the proposed Quantum MAS framework. Experimental results demonstrate a notable efficiency enhancement, with the proposed Quantum MAS achieving an average efficiency value of 0.92 across various experimental configurations and datasets. The findings underscore the significant potential of Quantum MAS in effectively addressing efficiency concerns within distributed agent environments, thus paving the way for broader applications of quantum computing in real-world scenarios.

DOI
10.3233/mgs-230127

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This paper explores a novel approach to distributed optimization leveraging the unique properties of quantum entanglement. Traditional distributed optimization methods often suffer from communication bottlenecks and reliance on shared information, limiting scalability and efficiency, particularly in scenarios with geographically dispersed agents. This research proposes a framework where agents collaborate to solve complex optimization problems through the utilization of quantum entanglement, effectively creating a "quantum entanglement network". The core idea is to exploit the non-local correlations inherent in entangled states for instantaneous information exchange, bypassing the need for direct communication channels. We detail a communication protocol based on entangled state manipulation and describe how an optimization algorithm can utilize this network to guide local decisions toward a global optimum. The theoretical framework demonstrates the potential for enhanced scalability and efficiency in distributed optimization, representing a significant departure from conventional approaches. The proposed method addresses the inherent limitations of classical communication in complex distributed systems, offering a fundamentally different paradigm for collaborative problem-solving. ---
With multi-agent systems advancing to high-dimensional and uncertain spaces, classical learning and control methods encounter fundamental challenges related to the curse of dimensionality and limited expressivity. Quantum computing offers the potential to overcome the shortcomings by embedding data into exponentially large Hilbert spaces, capturing complex correlations. We first propose a Distributed Quantum Gaussian Process (DQGP) framework enabling agents to collaboratively learn a high-fidelity global model of the environment through improved modeling capabilities and scalability. Numerical evaluations on non-stationary NASA SRTM datasets demonstrate the enhanced predictive and uncertainty estimation performance of DQGP compared to the classical Distributed Gaussian Processes. The findings lead to the next research phase: developing a Quantum-enhanced Learning Model Predictive Control architecture that results in robust, adaptive, and scalable coordination and control of multiple agents in complex scenarios.
This paper explores the potential of utilizing quantum entanglement for enhanced information encoding and rapid collaboration among multiple agents. The core claim is that leveraging the unique properties of quantum entanglement can overcome the limitations of classical communication, leading to significantly improved efficiency and speed. The proposed mechanism involves designing a novel encoding scheme based on entangled states, facilitating instantaneous synchronization and coordination among agents. This approach contrasts with traditional multi-agent systems reliant on classical communication, which are constrained by bandwidth and latency issues. The research presents a theoretical framework and outlines a strategy for achieving near-instantaneous information transfer, highlighting the transformative potential of quantum entanglement in collaborative systems. The key mathematical formulations and concepts are presented within this document.
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